AI-Powered Enterprise Intelligence Systems for Strategic Planning

  • Authors

    • Ole-Johan Dahl Professor, University of Oslo, Norway. Author
    • Kristen Nygaard Professor, University of Oslo, Norway. Author

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-2025PII8R1A

    Published 07-05-2025

  • Artificial Intelligence, Enterprise Intelligence Systems, Strategic Planning, Predictive Analytics, Machine Learning, Decision Support Systems, Business Intelligence

    Issue

    Vol. 8 No. 2 (2025)

    Section

    Articles

    How to Cite

    [1]
    O.-J. Dahl and K. Nygaard, “AI-Powered Enterprise Intelligence Systems for Strategic Planning”, IJAIDT, vol. 8, no. 2, pp. 01–15, Jul. 2025, doi: 10.67228/30713315/IJAIDT-2025PII8R1A.

  • Abstract

    Recent advancements in Artificial Intelligence (AI), machine learning, predictive analytics, and intelligent automation have transformed enterprise strategic planning. Traditional planning methods often relied on historical data, manual forecasting, and business intuition, resulting in delayed decisions and limited responsiveness. AI-driven Enterprise Intelligence Systems (EIS) address these challenges by integrating intelligent analytics, predictive forecasting, and real-time decision support into strategic planning processes. This research presents an AI-based Enterprise Intelligence framework that combines data integration, intelligent processing, predictive modeling, and strategic decision support. The system utilizes structured and unstructured data from ERP, CRM, supply chain, financial, and external market intelligence sources. Machine learning techniques, including regression models, neural networks, and ensemble algorithms, are employed to generate accurate business insights and forecasts. Performance evaluation demonstrates significant improvements, including a 35% increase in forecasting accuracy, a 42% reduction in decision-making time, a 38% improvement in operational efficiency, and a 40% enhancement in strategic planning effectiveness. The findings indicate that AI-driven Enterprise Intelligence Systems enable organizations to make informed decisions, optimize resources, respond rapidly to market changes, and achieve sustainable growth. The study concludes that AI-powered enterprise intelligence is a key enabler of future-ready strategic planning, competitive advantage, and digital transformation.

  • References

    [1] E. Turban, R. Sharda, and D. Delen, Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th ed. Boston, MA, USA: Pearson, 2021.

    [2] T. Davenport and J. Harris, Competing on Analytics: The New Science of Winning. Boston, MA, USA: Harvard Business Review Press, 2017.

    [3] Gajula, S. (2024). Cybersecurity risk prediction using graph neural networks. Journal of Information Systems Engineering and Management.

    [4] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Hoboken, NJ, USA: Pearson, 2020.

    [5] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.

    [6] F. Provost and T. Fawcett, Data Science for Business. Sebastopol, CA, USA: O'Reilly Media, 2013.

    [7] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed. Burlington, MA, USA: Morgan Kaufmann, 2011.

    [8] G. Shmueli, N. Patel, and P. Bruce, Data Mining for Business Analytics. Hoboken, NJ, USA: Wiley, 2018.

    [9] Gajula, S. (2024). Adaptive zero trust architecture for securing financial microservices. Computer Fraud & Security, 2024(12), 643–655. https://doi.org/10.52710/cfs.845

    [10] D. Power, “Decision Support Systems: Concepts and Resources for Managers,” Westport, CT, USA: Greenwood Publishing, 2002.

    [11] E. Turban, J. Aronson, and T. Liang, Decision Support Systems and Intelligent Systems. Upper Saddle River, NJ, USA: Prentice Hall, 2007.

    [12] T. Mitchell, Machine Learning. New York, NY, USA: McGraw-Hill, 1997.

    [13] C. Bishop, Pattern Recognition and Machine Learning. New York, NY, USA: Springer, 2006.

    [14] Gajula, S. (2023). A review of anomaly identification in finance frauds using machine learning system. International Journal of Current Engineering and Technology, 13(6), 568–575. https://ijcet.evegenis.org/index.php/ijcet/article/view/820

    [15] K. Murphy, Machine Learning: A Probabilistic Perspective. Cambridge, MA, USA: MIT Press, 2012.

    [16] IBM, “AI and Business Intelligence Report,” 2023. IBM Research

    [17] Gartner, “Top Strategic Technology Trends in Artificial Intelligence,” 2023. Gartner

    [18] McKinsey & Company, “The State of AI in Business Decision-Making,” 2023. McKinsey & Company

  • Downloads